As a baseline approach, we use the combination of kernel density estimation (KDE) (Gaussian kernel, \(0.2\)  bandwidth) and approximate Bayesian computation (ABC) [1]} with Euclidean distance as distance measure and uniform distribution over predefined parameter range as prior. We set the acceptance threshold such that the acceptance rate is equal to \(0.002\)  on average. To perform inference with ABC, we use precomputed training datasets. As an evaluation metric, we use log (natural) probability and normalized mean absolute error (MAE), i.e. scaled to a dimension range, between object parameters obtained by Randolph et al. [2]} and parameters with the highest log probability assigned by an inference algorithm.
